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Airway Inflammation in Children with Primary Ciliary Dyskinesia

2022· article· en· W4292369037 on OpenAlexaff
Scott D. Sagel, Oren Kupfer, Brandie D. Wagner, Stephanie D. Davis, Sharon Dell, Thomas W. Ferkol, Jordana E. Hoppe, Margaret Rosenfeld, Kelli M. Sullivan, Harm A.W.M. Tiddens, Michael R. Knowles, Margaret W. Leigh

Bibliographic record

VenueAnnals of the American Thoracic Society · 2022
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsUniversity of British Columbia
FundersNational Center for Advancing Translational SciencesRare Diseases Clinical Research NetworkNational Institutes of HealthNational Heart, Lung, and Blood InstituteGeorgia Clinical and Translational Science AllianceChildren's Hospital Colorado
KeywordsPrimary ciliary dyskinesiaMedicineAirwayInflammationIntensive care medicineImmunologyInternal medicineAnesthesiaLungBronchiectasis

Abstract

fetched live from OpenAlex

Abstract Rationale The role of airway inflammation in disease pathogenesis in children with primary ciliary dyskinesia (PCD) is poorly understood. Objectives We investigated relationships between sputum inflammation measurements, age, lung function, bronchiectasis, airway infection, and ultrastructural defects in children with PCD. Methods Spontaneously expectorated sputum was collected from clinically stable children and adolescents with PCD ages 6 years and older participating in a multicenter, observational study. Sputum protease and inflammatory cytokine concentrations were correlated with age, lung function, and chest computed tomography measures of structural lung disease, whereas differences in concentrations were compared between ultrastructural defect categories and between those with and without detectable bacterial infection. Results Sputum from 77 children with PCD (39 females [51%]; mean [standard deviation] age, 13.9 [4.9] yr; mean [standard deviation] forced expiratory volume in 1 second [FEV1]% predicted, 80.8 [20.5]) was analyzed. Sputum inflammatory marker measurements, including neutrophil elastase activity, IL-1β (interleukin-1β), IL-8, and TNF-α (tumor necrosis factor α) concentrations, correlated positively with age, percentage of bronchiectasis, and percentage of total structural lung disease on computed tomography, and negatively with lung function. Correlations between neutrophil elastase concentrations and FEV1% predicted and percentage of bronchiectasis were −0.32 (95% confidence interval, −0.51 to −0.10) and 0.46 (0.14 to 0.69), respectively. Sputum neutrophil elastase, IL-1β, and TNF-α concentrations were higher in those with detectable bacterial pathogens. Participants with absent inner dynein arm and microtubular disorganization had similar inflammatory profiles compared with participants with outer dynein arm defects. Conclusions In this multicenter pediatric PCD cohort, elevated concentrations of sputum proteases and cytokines were associated with impaired lung function and structural damage as determined by chest computed tomography, suggesting that sputum inflammatory measurements could serve as biomarkers in PCD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.359
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations23
Published2022
Admission routes1
Has abstractyes

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